Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_macro_f1_mean": 0.6937477912021662, | |
| "test_macro_f1_std": 0.004536414285693068, | |
| "test_macro_f1_text": "0.6937 ± 0.0045", | |
| "test_accuracy_mean": 0.6952117863720074, | |
| "test_accuracy_std": 0.003987225615950428, | |
| "test_accuracy_text": "0.6952 ± 0.0040", | |
| "test_macro_precision_mean": 0.6955007583676345, | |
| "test_macro_precision_std": 0.0053962198757247, | |
| "test_macro_precision_text": "0.6955 ± 0.0054", | |
| "test_macro_recall_mean": 0.6935251566187915, | |
| "test_macro_recall_std": 0.004259167819556978, | |
| "test_macro_recall_text": "0.6935 ± 0.0043", | |
| "dev_macro_f1_mean": 0.7248417464601334, | |
| "dev_macro_f1_std": 0.00291168417848945, | |
| "dev_macro_f1_text": "0.7248 ± 0.0029", | |
| "task": "ViFactCheck-full-context", | |
| "dataset": "ViFactCheck", | |
| "model_key": "cafebert", | |
| "model_name": "CafeBERT", | |
| "base_model": "uitnlp/CafeBERT", | |
| "seeds": [ | |
| 22, | |
| 42, | |
| 202 | |
| ], | |
| "representative_seed": 202, | |
| "selection_rule": "maximum development Macro-F1; seed ascending tie-break", | |
| "split_policy": "merged_stratified_80_10_10", | |
| "split_seed": 42, | |
| "max_length": 256, | |
| "epochs": 3, | |
| "effective_batch_size": 8 | |
| } |